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Multi-receptor skin with highly sensitive tele-perception somatosensory
Science Advances ( IF 11.7 ) Pub Date : 2024-09-11 , DOI: 10.1126/sciadv.adp8681
Yan Du 1, 2 , Penghui Shen 3 , Houfang Liu 3 , Yuyang Zhang 4 , Luyao Jia 1, 2 , Xiong Pu 1 , Feiyao Yang 1 , Tianling Ren 3 , Daping Chu 5 , Zhonglin Wang 1, 6, 7 , Di Wei 1, 5
Affiliation  

The limitations and complexity of traditional noncontact sensors in terms of sensitivity and threshold settings pose great challenges to extend the traditional five human senses. Here, we propose tele-perception to enhance human perception and cognition beyond these conventional noncontact sensors. Our bionic multi-receptor skin employs structured doping of inorganic nanoparticles to enhance the local electric field, coupled with advanced deep learning algorithms, achieving a Δ Vd sensitivity of 14.2, surpassing benchmarks. This enables precise remote control of surveillance systems and robotic manipulators. Our long short-term memory–based adaptive pulse identification achieves 99.56% accuracy in material identification with accelerated processing speeds. In addition, we demonstrate the feasibility of using a two-dimensional (2D) sensor matrix to integrate real object scan data into a convolutional neural network to accurately discriminate the shape and material of 3D objects. This promises transformative advances in human-computer interaction and neuromorphic computing.

中文翻译:


具有高度敏感的远程感知体感的多受体皮肤



传统非接触式传感器在灵敏度和阈值设置方面的局限性和复杂性,对扩展传统的人类五感提出了巨大挑战。在这里,我们提出了远程感知,以增强人类的感知和认知能力,超越这些传统的非接触式传感器。我们的仿生多受体皮肤采用无机纳米颗粒的结构化掺杂来增强局部电场,结合先进的深度学习算法,实现了 14.2 的 Δ V /Δ d 灵敏度,超越了基准。这实现了对监控系统和机器人机械手的精确远程控制。我们基于长短期记忆的自适应脉冲识别在材料识别方面实现了 99.56% 的准确率,并加快了处理速度。此外,我们还演示了使用二维 (2D) 传感器矩阵将真实物体扫描数据集成到卷积神经网络中以准确区分 3D 物体的形状和材料的可行性。这有望在人机交互和神经形态计算方面取得变革性进步。
更新日期:2024-09-11
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